A meta-analysis of flunixin concentrations in 65 horses from four countries estimated pharmacokinetic parameters. Considering the estimated plasma-to-urine ratio, raising the plasma International Screening Limit (ISL) from 1 to 3 ng/mL while keeping the urine ISL at 100 ng/mL could reduce the difference in detection times between the two matrices.
ABSTRACT
Flunixin meglumine is widely used to manage pain and inflammation in horses, and its regulation requires robust pharmacokinetic analysis for harmonization. In this study, we conducted a meta-analysis of flunixin disposition using plasma and urine concentration data from 65 horses across four countries to robustly estimate pharmacokinetic parameters in setting screening limits (SLs) for controlling medications in horses. A population (POP) model was developed using nonlinear mixed-effects model analysis. The irrelevant plasma concentration (IPC) and irrelevant urine concentration (IUC) were determined to be 1.9 and 70.2 ng/mL, respectively, with a typical urine-to-plasma ratio (Rss) of 35.9. Using the current International Federation of Horseracing Authorities (IFHA) screening limits (ISLs) (1 ng/mL for plasma; 100 ng/mL for urine), a longer detection time (DT) was observed for plasma than for urine, especially after multiple doses, as plasma ISL corresponds to a slower terminal elimination phase. Increasing the current plasma ISL from 1 to 3 ng/mL—while keeping the current urine ISL at 100 ng/mL—could better align the plasma and urine DTs. As a limitation of this study, both Standardbred and Thoroughbred data were included, and further data collection is needed to fully ascertain potential breed-specific effects. Moreover, this POP model also enabled relatively accurate Bayesian estimation of individual withdrawal times (WTs) from limited data. Clinicians could apply this Bayesian approach to making informed WT recommendations for horses when sufficient data is available. While existing non-POP statistical models remain viable, they may require a more conservative approach to WT estimation than Bayesian methods.
